Detection of circulating tumor cells using imaging flow cytometry

ABSTRACT

Photometric and morphometric features derived from multi-mode imagery of cells in flow are used as a cell analyzer to determine if a marker corresponding to a cancer cell or precancerous cell is present in the population of cells imaged. An imaging system simultaneously acquires a plurality of images for each cell passing through the field of view of the imaging system. Acquiring a plurality of different images (i.e., bright field, dark field, and fluorescent images) facilitates the determination of different morphological and morphometric parameters. Simultaneously acquiring the plurality of images enables relatively large populations of cells to be rapidly imaged, so that relatively small numbers of cancer cells in a large population of cells can be detected. Initially, known cancer cells are imaged to enable a marker to be identified. Then, a sample that may include cancer cells is imaged to determine if the marker is present.

RELATED APPLICATIONS

This application is a continuation of copending patent application Ser.No. 12/181,062, filed on Jul. 28, 2008, which itself is based on a priorprovisional application Ser. No. 60/952,522, filed on Jul. 27, 2007, thebenefit of the filing date of which is hereby claimed under 35 U.S.C.§119(e). Patent application Ser. No. 12/181,062 is acontinuation-in-part of a copending patent application Ser. No.11/344,941, filed on Feb. 1, 2006, which itself is based on a priorprovisional application Ser. No. 60/649,373, filed on Feb. 1, 2005, thebenefits of the filing dates of which are hereby claimed under 35 U.S.C.§119(e) and 35 U.S.C. §120.

Copending patent application Ser. No. 11/344,941 is also a continuationapplication based on a prior copending conventional application Ser. No.11/123,610, filed on May 4, 2005, which itself is based on a priorprovisional application Ser. No. 60/567,911, filed on May 4, 2004, andwhich is also a continuation-in-part of prior patent application Ser.No. 10/628,662, filed on Jul. 28, 2003, which issued as U.S. Pat. No.6,975,400 on Dec. 13, 2005, which itself is a continuation-in-partapplication of prior patent application Ser. No. 09/976,257, filed onOct. 12, 2001, which issued as U.S. Pat. No. 6,608,682 on Aug. 19, 2003,which itself is a continuation-in-part application of prior patentapplication Ser. No. 09/820,434, filed on Mar. 29, 2001, which issued asU.S. Pat. No. 6,473,176 on Oct. 29, 2002, which itself is acontinuation-in-part application of prior patent application Ser. No.09/538,604, filed on Mar. 29, 2000, which issued as U.S. Pat. No.6,211,955 on Apr. 3, 2001, which itself is a continuation-in-partapplication of prior application patent application Ser. No. 09/490,478,filed on Jan. 24, 2000, which issued as U.S. Pat. No. 6,249,341 on Jun.19, 2001, which itself is based on prior provisional patent applicationSer. No. 60/117,203, filed on Jan. 25, 1999, the benefit of the filingdates of which is hereby claimed under 35 U.S.C. §120 and 35 U.S.C.§119(e). Patent application Ser. No. 09/976,257, noted above, is alsobased on prior provisional application Ser. No. 60/240,125, filed onOct. 12, 2000, the benefit of the filing date of which is hereby claimedunder 35 U.S.C. §119(e).

BACKGROUND

Carcinomas are the most common form of cancer, and are responsible forthe majority of cancer-related deaths worldwide. Early detection ofcancer improves a prognosis significantly, as evidenced by the 70%reduction in mortality in cervical cancer after the Papanicolaou testbecame accepted as a routine annual examination in the United States.Likewise, mortality rates from breast cancer have been reduced by up to30% because of earlier detection through manual examination andmammograms. Unfortunately, the relative inaccessibility of most bodytissues currently limits the breadth of cancer screening. Even whentumors are detected by existing techniques and removed surgically, thereis a strong inverse correlation between tumor size and out-come, suchthat cancer survival rates are higher when tumors are detected early andremoved while the tumors are relatively small in size.

The analysis of accessible body fluids for the detection of neoplasticcells should greatly facilitate earlier cancer detection, and thedetection of micro-metastases in body fluids of patients who have earlystage cancer could have a substantial impact on optimizing therapeuticregimens and, thus, long-term prognosis. Unfortunately, even when canceris present in a patient, the relative number of cancer cells in readilyaccessible bodily fluids such as blood is generally quite small, makingcancer detection by sampling bodily fluids very challenging. Classicmicroscopy-based analysis, although the gold standard in diagnostics,lacks the throughput required to identify rare cell populationsconsistently and with confidence. Flow cytometry offers much higher dataacquisition rates, but flow cytometery depends largely on theavailability of fluorescently labeled markers to discriminate betweennormal cells and neoplastic cells, and tumor-specific markers generallyhave not yet been identified.

The use of an antibody-based approach to address this problem depends onectopic expression of a normal antigenic epitope, formation of a newepitope through genetic mutation or recombination, or consistentmodulation of the expression of a marker expressed in transformed andnon-transformed cells. The approach is confounded further by thediversity of neoplastic transformations and genetic heterogeneity in thehuman population.

In contrast to single- or multi-parameter antibody-based techniques,cellular morphology analysis is an effective means of cancer screening.For instance, dysplastic and neoplastic cells are detected in lungsputum on the basis of morphology. Likewise, exfoliated cells collectedfrom bladder washings of bladder cancer patients are shown to havedistinct morphologic and genetic changes. Dysplastic morphology is alsothe primary diagnostic criterion in Papanicolaou smears, wheremicroscope-based auto-mated morphologic analysis is shown to beeffective and approved by the Food and Drug Administration for primaryscreening.

Studies have indicated that cancer cells exhibit morphologicalcharacteristics that can be used to differentiate cancer cells fromnormal cells, however, most instruments capable of acquiring cellularimages having enough detail to enable such morphological characteristicsto be discerned do not have the throughput required to be able to detectvery small numbers of cancer cells hidden in relatively largepopulations of normal cells. This problem is significant, becausestudies have indicated that the blood of a majority of patients who havehad metastatic carcinomas contains fewer than one detectable carcinomacell per 7.5 mL of blood, which is below the current threshold of fivecirculating tumor cells necessary to make a statistically robustdiagnosis.

It would be desirable to provide a method and apparatus configured torapidly acquire detailed cellular images from relatively largepopulations of cells, such that relatively small numbers of cancer cellspresent in a larger population can be statistically detected.

SUMMARY

This application specifically incorporates herein by reference, thedisclosures and drawings of each patent application and issued patentidentified above as a related application.

The present disclosure provides methods of using both photometric andmorphometric features derived from multi-mode imagery of cells in flow.Such imaging methods can be employed for analyzing cells to determine ifa marker corresponding to a cancer cell or precancerous cell is presentin the population of cells imaged.

Preferably the population of cells is imaged while entrained in a fluidflowing through an imaging system. Imaging in flow enables image data torapidly be acquired from a relatively large population of cells.Furthermore, imaging cells in flow facilitates sample preparation, sincecells in bodily fluids can be imaged with very minimal samplepreparation.

The imaging system employed to acquire the image data for the populationof cells can be configured to simultaneously acquire a plurality ofimages for each cell passing through the field of view of the imagingsystem. Acquiring a plurality of different images is desirable, becauseutilizing different types of images (i.e., bright field images, darkfield images, and fluorescent images) facilitates the determination ofdifferent morphological and morphometric parameters. Indeed, some suchparameters cannot be obtained using only a single image. Simultaneouslyacquiring the plurality of different images is desirable becauseacquiring each different image at successive times would substantiallyincrease image acquisition time, meaning that acquiring image data for arelatively large population of cells would take much longer than wouldbe desirable.

Image data for a population of cells can be analyzed to detect cancer asfollows. First, one or more markers or characteristics that can bemeasured from images collected by the imaging system used to image thepopulation of cells is correlated to cancer cells (or precancerouscells). Once such a marker has been identified, a sample of bodily fluidfrom a patient can be very rapidly and easily analyzed to determine ifthat sample includes any cells having the identified marker.

An exemplary detection method includes the steps of using an imagingsystem to collect image data from a first population of biological cellswhere cancer or a precancerous condition is known to be present, andalso, to collect image data from a second population of biologicalcells, where the cell population includes only normal, healthy cells. Ifeither the healthy cells or the cancerous/precancerous cells arefluorescently labeled (and can therefore be distinguished using theimage data), the first and second cell populations can be combined andimaged together. At least one photometric or morphometric markerassociated with the cancerous condition is identified. Such a markerrelates to identifying a photometric and/or morphometric differencebetween healthy cells and cancerous/precancerous cells. As described ingreater detail below, exemplary markers include, but are not limited to,differences in the average nucleus size between healthy cells andcarcinoma cells, and differences in the images of healthy cells andcarcinoma cells. These differences can be quantified by processing theimage data for the population of cells.

Once a photometric and/or morphometric marker associated with thecancerous condition is identified, image data are collected from asample of a bodily fluid acquired from a patient (where it is not knownif the patient has cancer). Image data are collected for the sample, andthen the image data are analyzed to detect the presence of thepreviously identified marker, to determine whether cancer or aprecancerous condition is present in the sample from the patient.

Significantly, where the imaging systems described below are used tocollect the image data from a population of cells, the image data can becollected quite rapidly. In general, the analysis (i.e., analyzing thecollected image data to either initially identify a marker or todetermine the presence of a previously identified marker in a populationof cells) can be performed off-line, i.e., after the collection of theimage data. Current implementations of imaging processing software arecapable of analyzing a relatively large population of cells (e.g., tensof thousands of cells) within tens of minutes using readily availablepersonal computers. However, it should be recognized that as morepowerful computing systems are developed and become readily available,it may become possible to analyze the image data in real-time. Thus,off-line processing of the image data is intended to be exemplary,rather than limiting, and it is contemplated that real-time processingof the image data is an alternative.

It should be noted that different types of cancer will likely exhibitdifferent markers. Thus, the initial steps of analyzing images of knowncancer cells and normal cells will likely be repeated to identifymarkers for different types of cancer cells. Populations of abnormalcells that are not cancerous, but which may be indicative of aprecancerous condition (i.e., neoplastic cells), can also be imaged toidentify similar markers.

Aspects of the concepts disclosed herein relate to a system and methodfor imaging and analyzing biological cells entrained in a flow of fluid.In at least one exemplary embodiment, a plurality of images ofbiological cells are collected simultaneously. The plurality of imagesinclude at least two of the following types of images: a bright fieldimage, a dark field image, and a fluorescent image. Images are collectedfor a population of biological cells. Once the images have beencollected, the images can be processed to identify a subpopulation ofimages, where the subpopulation shares photometric and/or morphometriccharacteristics empirically determined to be associated with a cancerouscondition.

This Summary has been provided to introduce a few concepts in asimplified form that are further described in detail below in theDescription. However, this Summary is not intended to identify key oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

DRAWINGS

Various aspects and attendant advantages of one or more exemplaryembodiments and modifications thereto will become more readilyappreciated as the same becomes better understood by reference to thefollowing detailed description, when taken in conjunction with theaccompanying drawings, wherein:

FIG. 1A is a schematic diagram of an exemplary flow imaging system thatcan be used to simultaneously collect a plurality of images from anobject in flow;

FIG. 1B is another illustration of an exemplary flow imaging system forimplementing the concepts disclosed herein;

FIG. 1C is a schematic illustration of an exemplary imaging system forimplementing the concepts disclosed herein, wherein the cells to beimaged are disposed on a plate or slide;

FIG. 1D is a schematic illustration of a readout provided by a TDIdetector employed in an exemplary flow imaging system used in accordwith the concepts disclosed herein;

FIG. 2 is a pictorial representation of an image recorded by the flowimaging system of FIGS. 1A-1C;

FIG. 3 is a flow chart of the overall method steps implemented in oneaspect of the concepts disclosed herein;

FIG. 4 is an exemplary graphical user interface used to implement themethod steps of FIG. 3;

FIG. 5 is an exemplary graphical user interface used to implement themethod steps of FIG. 3 as applied to the analysis of human peripheralblood;

FIG. 6 includes images of normal (i.e., healthy) mammary epithelialcells;

FIG. 7 includes images of mammary carcinoma (i.e., diseased) cells,illustrating how quantification of data in a fluorescent channel servesas a marker for breast cancer;

FIG. 8A is an exemplary graphical user interface used to implement themethod steps of FIG. 3;

FIGS. 8B-8M are histograms illustrating a plurality of differentphotometric and morphometric descriptors that can be used toautomatically distinguish images of healthy mammary epithelial cellsfrom images of mammary carcinoma cells;

FIG. 9 graphically illustrates the separation of cells in humanperipheral blood into a variety of subpopulations based on photometricproperties;

FIG. 10A graphically illustrates a distribution of normal peripheralblood mononuclear cells (PBMC) based on image data collected from apopulation of cells that do not include mammary carcinoma cells;

FIG. 10B graphically illustrates a distribution of normal PBMC andmammary carcinoma cells based on image data collected from a populationof cells that includes both cell types, illustrating how thedistribution of mammary carcinoma cells is distinguishable from thedistribution of the normal PBMC cells;

FIG. 11A graphically illustrates a distribution of normal PBMC andmammary carcinoma cells based on measured cytoplasmic area derived fromimage data collected from a population of cells that includes both celltypes, illustrating how the distribution of cytoplasmic area of themammary carcinoma cells is distinguishable from the distribution ofcytoplasmic area of the normal PBMC;

FIG. 11B graphically illustrates a distribution of normal PBMC andmammary carcinoma cells based on measured scatter frequency derived fromimage data collected from a population of cells that includes both celltypes, illustrating how the distribution of the scatter frequency of themammary carcinoma cells is distinguishable from the distribution of thescatter frequency of the normal PBMC;

FIG. 12 is composite images of cells generated by combining bright fieldand fluorescent images of mammary carcinoma cells;

FIG. 13 illustrates representative images of five different PBMCpopulations that can be defined by scatter data derived from image dataof a population of cells;

FIG. 14 schematically illustrates an exemplary computing system used toimplement the method steps of FIG. 3;

FIG. 15 is a grayscale representation of human PBMC hybridized insuspension with a chromosome 12 probe, where each cell is represented bya row of four images, from left to right including dark field (blue in afull color version of the image), fluorescence from a chromosome12-SpectrumGreen probe (green in a full color version of the image),bright field (gray), and a superposition of the fluorescence and brightfield images, in which an unbiased selection of cells illustratesvariation in probe intensity, focus quality, and orientation withrespect to the optic axis;

FIG. 16 is a grayscale representation of Jurkat cells hybridized insuspension with a chromosome 8 probe and imaged in flow using standardoptics, wherein each cell is represented by a superposition of itschromosome 8 fluorescence (green in a full color version of the image)and bright field (gray) images; Jurkat cells are larger than human PBMCand exacerbate variations in image focus quality; and

FIG. 17 is a grayscale representation of Jurkat cells hybridized insuspension with a chromosome 8 probe and imaged in flow using EDFoptics, wherein each cell is represented by a superposition of itschromosome 8 fluorescence (green in a full color version of the image)and bright field (gray) images; EDF imaging greatly improves focusquality and the accuracy of FISH spot enumeration.

DESCRIPTION

Figures and Disclosed Embodiments are not Limiting

Exemplary embodiments are illustrated in referenced Figures of thedrawings. It is intended that the embodiments and Figures disclosedherein are to be considered illustrative rather than restrictive. Nolimitation on the scope of the technology and of the claims that followis to be imputed to the examples shown in the drawings and discussedherein.

With respect to the following disclosure, and the claims that follow, itshould be understood that the term population of cells refers to a groupof cells including a plurality of cells. Thus, a population of cellsmust include more than one cell.

The term cancer precursor is intended to refer to cell types that areabnormal but not cancerous.

The term multispectral images is intended to refer to images that areformed using light that has been spectrally dispersed (such as by aprism, where each different wavelength of light exits the prism at adifferent nominal angle) or spectrally decomposed (such as by a set offilters, where each filter emits a band of different wavelengths, suchas red light, or blue light).

The term multimodal images is intended to refer to images that areformed using different types of light from a cell. Fluorescent imagesare formed using light emitted by the cell in response to the excitationof a fluorophore (naturally present or added to the cell). Dark fieldimages and bright field images are formed using different illuminationtechniques, which are well known in the field of microscopy. Thus,fluorescent images, bright filed images, and dark field images eachrepresent imaging modes. Multimodal images must therefore include atleast two images acquired using a different mode.

The term morphometric parameter refers to a quantifiable parameterinvolving the shape of an object (i.e., a cell). Morphometricsfacilitates rigorous comparisons, enables complex shapes to be describedin a rigorous fashion, and permits numerical comparison betweendifferent shapes (i.e., cells). By reducing shape to a series ofnumbers, it allows objective comparisons. When applied to differenttypes of cells on a statistical basis, morphometric analysis canhighlight specific morphometric parameters that can be used todistinguish different types of cells.

The term photometric parameter refers to a quantifiable parameter thatcan be directly measured from an image, such as contrast, density, andcolor. Exemplary photometric parameters include, but are not limited to,nuclear optical density, cytoplasm optical density, background opticaldensity, and ratios of selected pairs of these values.

Overview

The present disclosure encompasses a method of using flow imagingsystems that can combine the speed, sample handling, and cell sortingcapabilities of flow cytometry with the imagery, sensitivity, andresolution of multiple forms of microscopy and full visible/nearinfrared spectral analysis, to collect and analyze data relating todisease conditions in blood, particularly for detecting cancerous cellsand precancerous cells.

A preferred imaging system to be used in collecting the image datarequired to implement the techniques disclosed herein will incorporatethe following principal characteristics:

1. high speed measurement;

2. the ability to process very large or continuous samples;

3. high spectral resolution and bandwidth;

4. good spatial resolution;

5. high sensitivity; and

6. low measurement variation.

In particular, recently developed imaging flow cytometer technology,embodied in an instrument marketed under the name ImageStream™ by AmnisCorporation, Seattle Wash., provides each of the above-noted principlecharacteristics. The ImageStream™ instrument is a commercial embodimentof the flow imaging system described below in detail with respect toFIG. 1A. Aspects of this imaging flow cytometer technology are describedin the following commonly assigned patents: U.S. Pat. No. 6,249,341,issued on Jun. 19, 2001, entitled “Imaging And Analyzing Parameters ofSmall Moving Objects Such As Cells;” U.S. Pat. No. 6,211,955 issued onApr. 3, 2001, also entitled “Imaging And Analyzing Parameters of SmallMoving Objects Such As Cells;” U.S. Pat. No. 6,473,176, issued on Oct.29, 2002, also entitled “Imaging And Analyzing Parameters of SmallMoving Objects Such As Cells;” U.S. Pat. No. 6,583,865, issued on Jun.24, 2003, entitled “Alternative Detector Configuration And Mode ofOperation of A Time Delay Integration Particle Analyzer;” and U.S.patent application Ser. No. 09/989,031 entitled “Imaging And AnalyzingParameters of Small Moving Objects Such As Cells in Broad Flat Flow.”While the ImageStream™ platform represents a particularly preferredimaging instrument used to acquire the image data that will be processedin accord with the concepts disclosed herein, it should be understoodthat the concepts disclosed herein are not limited only to the use ofthat specific instrument.

As noted above, in addition to collecting image data from a populationof biological cells, an aspect of the concepts disclosed herein involvesprocessing the image data collected to determine if any of the imagedcells in the population exhibit one or more characteristics associatedwith cancer or a precancerous condition. A preferred image analysissoftware package is IDEAS™ (Amnis Corporation, Seattle Wash.). TheIDEAS™ package evaluates 250 features for every cell, including multiplemorphologic and fluorescence intensity measurements, which can be usedto define and characterize cell populations. The IDEAS™ package enablesthe user to define biologically relevant cell subpopulations, andanalyze subpopulations using standard cytometry analyses, such as gatingand backgating. It should be understood, however, that other imageanalysis methods or software packages can be implemented to apply theconcepts disclosed herein, and the preferred image analysis softwarepackage that is disclosed is intended to be exemplary, rather thanlimiting of the concepts disclosed herein.

Overview of a Preferred Imaging System

FIG. 1A is a schematic diagram of a preferred flow imaging system 510(functionally descriptive of the ImageStream™ platform) that uses TDIwhen capturing images of objects 502 (such as biological cells),entrained in a fluid flow 504. System 510 includes a velocity detectingsubsystem that is used to synchronize a TDI imaging detector 508 withthe flow of fluid through the system. Significantly, imaging system 510is capable of simultaneously collecting a plurality of images of anobject. A particularly preferred implementation of imaging system 510 isconfigured for multi-spectral imaging and can operate with six spectralchannels: DAPI fluorescence (400-460 nm), Dark field (460-500 nm), FITCfluorescence (500-560 nm), PE fluorescence (560-595 nm), Bright field(595-650 nm), and Deep Red (650-700 nm). The TDI detector can provide 10bit digital resolution per pixel. The numeric aperture of the preferredimaging system is typically 0.75, with a pixel size of approximately 0.5microns. However, those skilled in the art will recognize that this flowimaging system is neither limited to six spectral channels, nor limitedto either the stated aperture size or pixel size and resolution.

Moving objects 502 are illuminated using a light source 506. The lightsource may be a laser, a light emitting diode, a filament lamp, a gasdischarge arc lamp, or other suitable light emitting source, and thesystem may include optical conditioning elements such as lenses,apertures, and filters that are employed to deliver broadband or one ormore desired wavelengths or wavebands of light to the object with anintensity required for detection of the velocity and one or more othercharacteristics of the object. Light from the object is split into twolight paths by a beam splitter 503. Light traveling along one of thelight paths is directed to the velocity detector subsystem, and lighttraveling along the other light path is directed to TDI imaging detector508. A plurality of lenses 507 are used to direct light along the pathsin a desired direction, and to focus the light. Although not shown, afilter or a set of filters can be included to deliver to the velocitydetection subsystem and/or TDI imaging detector 508, only a narrow bandof wavelengths of the light corresponding to, for example, thewavelengths emitted by fluorescent or phosphorescent molecules in/on theobject, or light having the wavelength(s) provided by the light source506, so that light from undesired sources is substantially eliminated.

The velocity detector subsystem includes an optical grating 505 a thatamplitude modulates light from the object, a light sensitive detector505 b (such as a photomultiplier tube or a solid-state photodetector), asignal conditioning unit 505 c, a velocity computation unit 505 d, and atiming control unit 505 e, which assures that TDI imaging detector 508is synchronized to the flow of fluid 504 through the system. The opticalgrating preferably comprises a plurality of alternating transparent andopaque bars that modulate the light received from the object, producingmodulated light having a frequency of modulation that corresponds to thevelocity of the object from which the light was received. Preferably,the optical magnification and the ruling pitch of the optical gratingare chosen such that the widths of the bars are approximately the sizeof the objects being illuminated. Thus, the light collected from cellsor other objects is alternately blocked and transmitted through theruling of the optical grating as the object traverses the interrogationregion, i.e., the field of view. The modulated light is directed towarda light sensitive detector, producing a signal that can be analyzed by aprocessor to determine the velocity of the object. The velocitymeasurement subsystem is used to provide timing signals to TDI imagingdetector 508.

Preferably, signal conditioning unit 505 c comprises a programmablecomputing device, although an ASIC chip or a digital oscilloscope canalso be used for this purpose. The frequency of the photodetector signalis measured, and the velocity of the object is computed as a function ofthat frequency. The velocity dependent signal is periodically deliveredto a TDI detector timing control 505 e to adjust the clock rate of TDIimaging detector 508. Those of ordinary skill in the art will recognizethat the TDI detector clock rate is adjusted to match the velocity ofthe image of the object over the TDI detector to within a smalltolerance selected to ensure that longitudinal image smearing in theoutput signal of the TDI detector is within acceptable limits. Thevelocity update rate must occur frequently enough to keep the clockfrequency within the tolerance band as flow (object) velocity varies.

Beam splitter 503 has been employed to divert a portion of light from anobject 502 to light sensitive detector 505 b, and a portion of lightfrom object 502 a to TDI imaging detector 508. In the light pathdirected toward TDI imaging detector 508, there is a plurality ofstacked dichroic filters 509, which separate light from object 502 ainto a plurality of wavelengths. One of lenses 507 is used to form animage of object 502 a on TDI imaging detector 508.

The theory of operation of a TDI detector like that employed in system510 is as follows. As objects travel through a flow tube 511 (FIG. 1A)and pass through the volume imaged by the TDI detector, light from theobjects forms images of the objects, and these images travel across theface of the TDI detector. The TDI detector preferably comprises a chargecoupled device (CCD) array, which is specially designed to allow chargeto be transferred on each clock cycle, in a row-by-row format, so that agiven line of charge remains locked to, or synchronized with, a line inthe image. The row of charge is clocked out of the array and into amemory when it reaches the bottom of the array. The intensity of eachline of the signal produced by the TDI detector corresponding to animage of an object is integrated over time as the image andcorresponding resulting signal propagate over the CCD array. Thistechnique greatly improves the signal-to-noise ratio of the TDI detectorcompared to non-integrating type detectors—a feature of great benefit ina detector intended to respond to images from low-level fluorescenceemission of an object. Proper operation of the TDI detector requiresthat the charge signal be clocked across the CCD array insynchronization with the rate at which the image of the object movesacross the CCD array. An accurate clock signal to facilitate thissynchronization can be provided by determining the velocity of theobject, and the concepts disclosed herein use an accurate estimate ofthe object's velocity, and thus, of the velocity of the image as itmoves over the CCD array of the TDI detector. A flow imaging system ofthis type is disclosed in commonly assigned U.S. Pat. No. 6,249,341, thecomplete disclosure, specification, and drawings of which are herebyspecifically incorporated herein by reference.

In a preferred implementation, cells are hydrodynamically focused into asingle-file line in a fluidic system (not separately shown), forming atall but narrow field of view. This technique enables the lateraldimension of the detector to be used for signal decomposition. Thisaspect of the preferred imaging system (i.e., ImageStream™) can bereadily visualized in FIG. 1B. Cells 99 are hydrodynamically focused ina flow of fluid directed into a flow cuvette 116 and illuminated fromone or more sides using light sources 98 and 100. Light is collectedfrom the cells with a high NA objective lens 102, and the light that iscollected is directed along a light path including lenses 103A and 103B,and a slit 105. A fraction of this collected light is transmitted to anauto-focus subsystem 104 and to a velocity detection system 106. Itshould be noted that in connection with an imaging system that uses aTDI detector, it is important to ensure the data signal produced by thedetection system, which is integrated over time to increase thesignal-to-noise ratio, is properly synchronized with the flow of cellsthrough the imaging system.

Optional distortion elements can be included in the flow imaging system,to alter the optical wave front of light from the cells in adeterministic way. The combination of a modified wave front andpost-processing of the imagery enables extended depth of field (EDF)images to be obtained by the imaging system. Either an opticaldistortion element 5A is disposed between the objects being imaged andthe collection lens, or an optical distortion element 5B is disposed ininfinite space (that is, at the objective aperture or at a conjugateimage of the aperture at a subsequent location in the optical system,but before the detector). Alternatively, optical distortion may beintroduced via adjustment of a correction collar on an adjustableimplementation of objective lens 102. Only one means of introducingoptical distortion is required. The function of the optical distortionis to change the light from the object to achieve a point spreadfunction (PSF) that is substantially invariant across an EDF, such thatnegative effects of the distortion produced by the element cansubsequently be removed by signal processing, to yield an EDF image.Another technique that can be used to introduce optical distortion intolight from the object is to use a cuvette/flow cell having differentoptical thicknesses at different locations, such that imaging throughthe different locations of the cuvette induces different degrees of wavefront deformation. For example, different faces of the cuvette caninduce different levels of distortion, with one or more facesintroducing no intentional distortion/deformation, with other facesconfigured to intentionally deform the optical wave front of light fromthe object. Moving the cuvette relative to the imaging optical systemenables the deformation to be selectively induced. An optional cuvettemanipulator 9 for manipulating the position of the cuvette relative tothe optical system is shown in FIG. 1B. Where different faces of thecuvette induce different levels of deformation, such means willgenerally rotate the cuvette. It should also be recognized that a singleface of a cuvette can induce different levels of deformation atdifferent locations, such that translating the cuvette linearly caninduce different levels of deformation. In such an embodiment,manipulator 9 will be configured to translate the cuvette linearly.Those of ordinary skill in the art will recognize that many differentstructural configurations can be used to implement manipulator 9, suchas stepper motors, linear actuators, hydraulics, powered hinges, poweredlinkages, and others. The specific configuration is not critical, solong as manipulation of the cuvette does not introduce additionaloptical errors beyond the intentional deformation, thus the specifiedstructures for manipulator 9 should be considered exemplary, rather thanlimiting.

The majority of the light is passed to a spectral decomposition element108, which employs a fan-configuration of dichroic mirrors 110 to directdifferent spectral bands laterally onto different regions of a TDIdetector 114. Thus, the imaging system is able to decompose the image ofa single cell 118 into multiple sub-images 120 across detector 114, eachsub-image corresponding to a different spectral component. In this view,detector 114 has been enlarged and is shown separately to highlight itselements. Note that the different spectral or sub images are dispersedacross the detector orthogonally relative to a direction of motion ofthe images across the detector, as indicated by an arrow 111.

Spectral decomposition greatly facilitates the location, identification,and quantification of different fluorescence-labeled biomolecules withina cell by isolating probe signals from each other, and from backgroundauto fluorescence. Spectral decomposition also enables simultaneousmultimode imaging (bright field, dark field, etc.) using band-limitedlight in channels separate from those used for fluorescence imaging.FIG. 1B illustrates an exemplary flow-based embodiment of flow imagingsystem 150. However, it should be recognized that such an imaging systemcan be configured to collect images of objects on a plate or slide 7,where the plate/slide moves relative to the imaging system, instead ofthe flow-based embodiment, as indicated in FIG. 1C.

It should be recognized that other elements (such as a prism or a filterstack) could be similarly employed to spectrally disperse the light, andthe dichroic mirrors simply represent an exemplary implementation. Flowimaging system 150 can employ a prism (not shown) or a grating orientedto disperse light laterally with regard to the axis of flow prior to thefinal focusing optics, for spectral analysis of each object's intrinsicfluorescence. In yet another exemplary embodiment of a suitable flowimaging system that is contemplated (but not shown), a cylindrical finalfocusing lens can be employed to image a Fourier plane on the detectorin the cross-flow axis, enabling analysis of the light scatter angle.These techniques for multi-spectral imaging, flow spectroscopy, andFourier plane scatter angle analysis can be employed simultaneously bysplitting the collected light into separate collection paths, withappropriate optics in each light path. For enhanced morphology or toanalyze forward scatter light, a second imaging objective and collectiontrain can be used to image the particles through an orthogonal facet ofthe flow cuvette 116, thereby viewing the objects in stereoscopicperspective with no loss of speed or sensitivity.

To analyze the collected imagery, a software based image analysisprogram can be employed. One example of suitable image analysis softwareis the IDEAS™ package (available from Amnis Corporation, Seattle,Wash.). The IDEAS™ software package evaluates over 200 quantitativefeatures for every cell, including multiple morphologic and fluorescenceintensity measurements, which can be used to define and characterizecell populations. The IDEAS™ software package enables the user to definebiologically relevant cell subpopulations, and analyze subpopulationsusing standard cytometry analyses, such as gating and backgating. Itshould be understood, however, that other image analysis methods orsoftware packages can be employed to apply the concepts disclosedherein, and the IDEAS™ image analysis software package is intended to bemerely one example of a suitable software for this purpose, rather thanlimiting on the concepts disclosed herein.

Turning now to FIG. 1D, detector 114 of the exemplary flow imagingsystem shown in FIG. 1B is implemented using a TDI that performs highthroughput imaging with high sensitivity. As shown in an exemplaryreadout 138, the image on the TDI detector is read out one row of pixelsat a time from the bottom of the detector. After each row is read out,the signals in the remaining detector pixels are shifted down by onerow. The readout/shift process repeats continuously, causing latentimage 142 to translate down the detector during readout (note themovement of latent image 142 through frames T1-T6). If the readout rateof the TDI detector is matched to the velocity of the object beingimaged, the image does not blur as it moves down the TDI detector. Ineffect, the TDI detector electronically “pans” the rate at which rowsare read out to track the motion of an object being imaged. To provideoptimum results for this technique, it is important to accuratelymeasure the velocity of the objects being imaged and to employ thatmeasurement in feedback control of the TDI readout rate. Thus, accuratevelocity detection for objects moving in flow enables the TDI imaging tobe implemented properly.

One primary advantage of TDI detection over other methods is the greatlyincreased image integration period it provides. An exemplary flowimaging system used in connection with the present invention includes aTDI detector that has 512 rows of pixels, provides a commensurate 512×increase in signal integration time. This increase enables the detectionof even faint fluorescent probes within cell images and intrinsic autofluorescence of cells acquired at a high-throughput.

Furthermore, the use of a TDI detector increases measured signalintensities up to a thousand fold, representing over a 30 foldimprovement in the signal-to-noise ratio compared to other methodsdisclosed in the prior art. This increased signal intensity enablesindividual particles to be optically addressed, providinghigh-resolution measurement of either scattered spectral intensity ofwhite light or scattered angular analysis of monochromatic light ofselected wavelengths.

Exemplary flow imaging system 150 can be configured for multi-spectralimaging and can operate with, for example, six spectral channels: DAPIfluorescence (400-460 nm), Dark field (460-500 nm), FITC fluorescence(500-560 nm), PE fluorescence (560-595 nm), Bright field (595-650 nm),and Deep Red (650-700 nm). The TDI detector can provide 10 bit digitalresolution per pixel. The NA of the exemplary imaging system istypically about 0.75, with a pixel size of approximately 0.5 microns.However, those skilled in the art will recognize that this flow imagingsystem is neither limited to six spectral channels nor limited to eitherthe stated NA, or pixel size and resolution.

FIG. 2 is a pictorial representation of images produced by the exemplaryflow imaging systems of FIGS. 1A-1C. It should be recognized that whileFIG. 2 is based on a full color image, that image has been manipulatedto facilitate reproduction. The colors in the original image werereversed (i.e., a negative of the original image was obtained), thenthat negative was converted from a color image to a grayscale image, andcontrast adjustments were performed. Thus, FIG. 2 is provided toindicate the types of cellular images that can be acquired, as opposedto faithfully reproducing actual cellular images in their original form.A column 520, labeled “BF,” includes images created by the absorption oflight from light source 506 by spherical objects 502 entrained in fluidflow 504. The “BF” label refers to “bright field,” a term derived from amethod for creating contrast in an image whereby light is passed througha region and the absorption of light by objects in the region producesdark areas in the image. The background field is thus bright, while theobjects are dark in this image. Thus, column 520 is the “bright fieldchannel” It should be understood that the inclusion of a bright fieldimage is exemplary, rather than limiting on the scope of the conceptsdisclosed herein. Preferably, the concepts disclosed herein utilize acombination of bright field images and fluorescent images, or of darkfield images and fluorescent images.

The remaining three columns 522, 524, and 526 shown in FIG. 2 arerespectively labeled “λ1,” “λ2,” and “λ3.” These columns include imagesproduced using light that has been emitted by an object entrained in thefluid flow. Preferably, such light is emitted through the process offluorescence (as opposed to images produced using transmitted light). Asthose of ordinary skill in the art will recognize, fluorescence is theemission of light (or other electromagnetic radiation) by a substancethat has been stimulated by the absorption of incident radiation.Generally, fluorescence persists only for as long as the stimulatingradiation persists. Many substances (particularly fluorescent dyes) canbe identified based on the spectrum of the light that is produced whenthey fluoresce. Columns 522, 524, and 526 are thus referred to as“fluorescence channels.”

As noted above, additional exemplary flow imaging systems are disclosedin commonly assigned U.S. Pat. No. 6,211,955 and U.S. Pat. No.6,608,682, the complete disclosure, specification, and drawings of whichare hereby specifically incorporated herein by reference as backgroundmaterial. The imaging systems described above and in these two patentsin detail, and incorporated herein by reference, have substantialadvantages over more conventional systems employed for the acquisitionof images of biological cell populations. These advantages arise fromthe use in several of the imaging systems of an optical dispersionsystem, in combination with a TDI detector that produces an outputsignal in response to the images of cells and other objects that aredirected onto the TDI detector. Significantly, multiple images of asingle object can be collected at one time. The image of each object canbe spectrally decomposed to discriminate object features by absorption,scatter, reflection, or emissions, using a common TDI detector for theanalysis. Other systems include a plurality of detectors, each dedicatedto a single spectral channel

These imaging systems can be employed to determine morphological,photometric, and spectral characteristics of cells and other objects bymeasuring optical signals including light scatter, reflection,absorption, fluorescence, phosphorescence, luminescence, etc.Morphological parameters include area, perimeter, texture or spatialfrequency content, centroid position, shape (i.e., round, elliptical,barbell-shaped, etc.), volume, and ratios of selected pairs (or subsets)of these parameters. Similar parameters can also be determined for thenuclei, cytoplasm, or other sub-compartments of cells with the conceptsdisclosed herein. Photometric measurements with the preferred imagingsystem enable the determination of nuclear optical density, cytoplasmoptical density, background optical density, and ratios of selectedpairs of these values. An object being imaged with the conceptsdisclosed herein can either be stimulated into fluorescence orphosphorescence to emit light, or may be luminescent, producing lightwithout stimulation. In each case, the light from the object is imagedon the TDI detector to use the concepts disclosed herein to determinethe presence and amplitude of the emitted light, the number of discretepositions in a cell or other object from which the light signal(s)originate(s), the relative placement of the signal sources, and thecolor (wavelength or waveband) of the light emitted at each position inthe object.

Using a Multispectral Imaging System to Analyze a Bodily Fluid forCancer Cells

As noted above, aspects of the concepts disclosed herein involve boththe collection of multispectral images from a population of biologicalcells, and the analysis of the collected images to identify at least onephotometric or morphological feature that has been empiricallydetermined to be associated with cancer cells or precancerous cells.Thus, an aspect of the present disclosure relates to the use of bothphotometric and morphometric features derived from multi-mode imagery ofcells in flow to discriminate cell features in populations of cells, tofacilitate the detection of the presence of cancer or a precancerouscondition. Discussed in more detail below are methods for analyzingcells in suspension or flow, which may be combined with comprehensivemultispectral imaging to provide morphometric and photometric data toenable, for example, the quantization of characteristics exhibited byboth normal cells and cancer/precancerous cells, to facilitate thedetection of cancer or abnormal cells indicative of a precancerouscondition. Heretofore, such methods have not been feasible with standardmicroscopy and/or flow cytometry.

As noted above, a preferred flow imaging system (e.g., the ImageStream™platform) can be used to simultaneously acquire multispectral images ofcells in flow, to collect image data corresponding to bright field, darkfield, and four channels of fluorescence. The ImageStream™ platform is acommercial embodiment based on the imaging systems described in detailabove. In general, cells are hydrodynamically focused into a core streamand orthogonally illuminated for both dark field and fluorescenceimaging. The cells are simultaneously trans-illuminated via aspectrally-limited source (e.g., filtered white light or a lightemitting diode) for bright field imaging. Light is collected from thecells with an imaging objective lens and is projected on a CCD array.The optical system has a numeric aperture of 0.75 and the CCD pixel sizein object space is 0.5μ², enabling high resolution imaging at eventrates of approximately 100 cells per second. Each pixel is digitizedwith 10 bits of intensity resolution in this example, providing aminimum dynamic range of three decades per pixel. In practice, thespread of signals over multiple pixels results in an effective dynamicrange that typically exceeds four decades per image. Additionally, thesensitivity of the CCD can be independently controlled for eachmultispectral image, resulting in a total of approximately six decadesof dynamic range across all the images associated with an object. Itshould be understood that while the ImageStream™ platform represents aparticularly preferred flow imaging system for acquiring image data inaccord with the concepts disclosed herein, the ImageStream™ platform isintended to represent an exemplary imaging system, rather than limitingthe concepts disclosed. Any imaging instrument capable of collectingimages of a population of biological cells sufficient to enable theimage analysis described in greater detail below to be achieved can beimplemented in accord with the concepts presented herein.

Referring again to the preferred imaging system, the ImageStream™platform, prior to projection on the CCD, the light is passed through aspectral decomposition optical system that directs different spectralbands to different lateral positions across the detector (such spectraldecomposition is discussed in detail above in connection with thedescription of the various preferred embodiments of imaging systems).With this technique, an image is optically decomposed into a set of aplurality of sub-images (preferably 6 sub-images, including: brightfield, dark field, and four different fluorescent images), eachsub-image corresponding to a different spectral (i.e., color) componentand spatially isolated from the remaining sub-images. This processfacilitates identification and quantization of signals within the cellby physically separating on the detector signals that may originate fromoverlapping regions of the cell. Spectral decomposition also enablesmultimode imaging, i.e., the simultaneous detection of bright field,dark field, and multiple colors of fluorescence. The process of spectraldecomposition occurs during the image formation process, rather than viadigital image processing of a conventional composite image.

The CCD may be operated using TDI to preserve sensitivity and imagequality even with fast relative movement between the detector and theobjects being imaged. As with any CCD, image photons are converted tophoto charges in an array of pixels. However, in TDI operation, thephoto charges are continuously shifted from pixel to pixel down thedetector, parallel to the axis of flow. If the photo charge shift rateis synchronized with the velocity of the image of the cell, the effectis similar to physically panning a camera. Image streaking is avoideddespite signal integration times that are orders of magnitude longerthan in conventional flow cytometry. For example, an instrument mayoperate at a continuous data rate of approximately 30 mega pixels persecond and integrate signals from each object for 10 milliseconds,enabling the detection of even faint fluorescent probes within cellimages to be acquired at relatively high speed. Careful attention topump and fluidic system design to achieve highly laminar, non-pulsatileflow eliminates any cell rotation or lateral translation on the timescale of the imaging process (see, e.g., U.S. Pat. No. 6,532,061).

A real-time algorithm analyzes every pixel read from the CCD to detectthe presence of object images and calculate a number of basicmorphometric and photometric features, which can be used as criteria fordata storage. Data files encompassing 10,000-20,000 cells are typicallyabout 100 MB in size and, therefore, can be stored and analyzed usingstandard personal computers. The TDI readout process operatescontinuously without any “dead time,” which means every cell can beimaged and the coincidental imaging of two or more cells at a timeeither in contact or not, presents no barrier to data acquisition.

Such an imaging system can be employed to determine morphological,photometric, and spectral characteristics of cells and other objects bymeasuring optical signals, including light scatter, reflection,absorption, fluorescence, phosphorescence, luminescence, etc. As usedherein, morphological parameters (i.e., morphometrics) may be basic(e.g., nuclear shape) or may be complex (e.g., identifying cytoplasmsize as the difference between cell size and nuclear size). For example,morphological parameters may include nuclear area, perimeter, texture orspatial frequency content, centroid position, shape (i.e., round,elliptical, barbell-shaped, etc.), volume, and ratios of selected pairsof these parameters. Morphological parameters of cells may also includecytoplasm size, texture or spatial frequency content, volume, and thelike. As used herein, photometric measurements with the aforementionedimaging system can enable the determination of nuclear optical density,cytoplasm optical density, background optical density, and the ratios ofselected pairs of these values. An object being imaged can be stimulatedinto fluorescence or phosphorescence to emit light, or may beluminescent, wherein light is produced by the object withoutstimulation. In each case, the light from the object may be imaged on aTDI detector of the imaging system to determine the presence andamplitude of the emitted light, the number of discrete positions in acell or other object from which the light signal(s) originate(s), therelative placement of the signal sources, and the color (wavelength orwaveband) of the light emitted at each position in the object.

The present disclosure provides methods of using both photometric andmorphometric features derived from multi-mode imagery of cells in flow.Such methods can be employed as a cell analyzer to determine if a markercorresponding to a cancer cell or precancerous cell is present in thepopulation of cells imaged. Preferably the population of cells is imagedwhile entrained in a fluid flowing through an imaging system. As usedherein, gating refers to a subset of data relating to photometric ormorphometric imaging. For example, a gate may be a numerical orgraphical boundary of a subset of data that can be used to define thecharacteristics of particles to be further analyzed. Here, gates havebeen defined, for example, as a plot boundary that encompasses “infocus” cells, or sperm cells with tails, or sperm cells without tails,or cells other than sperm cells, or sperm cell aggregates, or celldebris. Further, backgating may be a subset of the subset data. Forexample, a forward scatter versus a side scatter plot in combinationwith a histogram from an additional marker may be used to backgate asubset of cells within the initial subset of cells.

Many of the applications of an imaging system as described herein willrequire that one or more light sources be used to provide light that isincident on the object being imaged. A person having ordinary skill inthe art will know that the locations of the light sources substantiallyaffect the interaction of the incident light with the object and thekind of information that can be obtained from the images using adetector.

In addition to imaging an object with the light that is incident on it,a light source can also be used to stimulate emission of light from theobject. For example, a cell having been contacted with a probeconjugated to a fluorochrome (e.g., such as FITC, PE, APC, Cy3, Cy5, orCy5.5) will fluoresce when excited by light, producing a correspondingcharacteristic emission spectra from any excited fluorochrome probe thatcan be imaged on a TDI detector. Light sources may alternatively be usedfor causing the excitation of fluorochrome probes on an object, enablinga TDI detector to image fluorescent spots produced by the probes on theTDI detector at different locations as a result of the spectraldispersion of the light from the object that is provided by a prism. Thedisposition of these fluorescent spots on the TDI detector surface willdepend upon their emission spectra and their location in the object.

Each light source may produce light that can either be coherent,non-coherent, broadband, or narrowband light, depending upon theapplication of the imaging system desired. Thus, a tungsten filamentlight source can be used for applications in which a narrowband lightsource is not required. For applications such as stimulating theemission of fluorescence from probes, narrowband laser light ispreferred, since it also enables a spectrally decomposed, non-distortedimage of the object to be produced from light scattered by the object.This scattered light image will be separately resolved from thefluorescent spots produced on a TDI detector, so long as the emissionspectra of any of the spots are at different wavelengths than thewavelength of the laser light. The light source can be either of thecontinuous wave (CW) or pulsed type, such as a pulsed laser. If a pulsedtype illumination source is employed, the extended integration periodassociated with TDI detection can enable the integration of signals frommultiple pulses. Furthermore, it is not necessary for the light to bepulsed in synchronization with the TDI detector.

Particularly for use in collecting image data for cell populations foundin bodily fluids such as blood, it can be desirable to employ a 360 nmUV laser as a light source, and to optimize the optical system of theimaging system for diffraction-limited imaging performance in the400-460 nm (DAPI emission) spectral band.

In embodiments consistent with the disclosure herein, it is to beunderstood that relative movement exists between the object being imagedand the imaging system. In most cases, it will be more convenient tomove the object than to move the imaging system. It is also contemplatedthat in some cases, the object may remain stationary and the imagingsystem move relative to it. As a further alternative, both the imagingsystem and the object may be in motion, which movement may be indifferent directions and/or at different rates.

Exemplary High Level Method Steps

FIG. 3 is a flow chart 400 schematically illustrating exemplary stepsthat can be used to detect cancer (or a precancerous condition) byanalyzing a population of cells collected from a bodily fluid (such asblood), based on images of the cell population. First, one or moremarkers or characteristics that can be measured from images collected bythe imaging system used to image the population of cells must becorrelated to cancer cells (or precancerous cells). Once such a markerhas been identified, a sample of bodily fluid from a patient can be veryrapidly and easily analyzed to determine if that sample includes anycells having the identified marker.

In a block 402, an imaging system, such as the exemplary imaging systemsdescribed above in detail, is used to collect image data from a firstpopulation of biological cells where cancer or a precancerous conditionis known to be present.

In a block 404, the imaging system is used to collect image data from asecond population of biological cells, where the cell populationincludes only normal, healthy cells. If either the healthy cells or thecancerous/precancerous cells are fluorescently labeled, the first andsecond cell populations can be combined and imaged together.

In a block 406 at least one photometric or morphometric markerassociated with the cancerous condition is identified. The markerrelates to identifying a photometric and/or morphometric differencebetween healthy cells and cancerous/precancerous cells. As will bedescribed in greater detail below, such markers include differences inthe average nucleus size between healthy cells and carcinoma cells, anddifferences in images of healthy cells and carcinoma cells. Thesedifferences can be quantified based on processing the image data for thepopulation of cells, to identify images that are more likely to beimages of carcinoma cells, and to identify images that are more likelyto be images of healthy cells.

Once a photometric and/or morphometric marker associated with thecancerous condition is identified, image data are collected from asample of a bodily fluid acquired from a patient, where it is not knownwhether or not the patient has cancer. In a block 408 a sample of bodilyfluid from a patient is obtained. In a block 410 image data arecollected for the sample, and then the image data are analyzed in ablock 412 for the presence of the previously identified marker, todetermine whether cancer or a precancerous condition is present in thesample from the patient.

It should be noted that different types of cancer will likely exhibitdifferent markers, thus the steps of blocks 402 and 406 will likely berepeated to identify markers for different types of cancer cells.Populations of abnormal cells that are not cancerous, but which may beindicative of a precancerous condition (i.e., neoplastic cells) can alsobe imaged to identify similar markers. Benign neoplastic cell massesinclude uterine fibroids and skin moles. These types of neoplastic cellsdo not transform into cancer. Potentially malignant neoplasms includecarcinoma in situ. Given time, these neoplastic cell types will likelytransform into a cancer, and thus are indicative of a precancerouscondition (malignant neoplasms are commonly referred to as cancer; andinvade and destroy the surrounding tissue and may metastasize). Thus,the steps of blocks 402 and 406 may also be optimized to identifymarkers to look for potentially malignant neoplastic cells, in additionto cancer cells.

While not strictly required, in a working embodiment of the techniquesdescribed herein, additional processing was implemented to reducecrosstalk and spatial resolution for the multi-channel imaging. Thecrosstalk reduction processing implemented is described in commonlyassigned U.S. Pat. No. 6,763,149, the specification, disclosure and thedrawings of which are hereby specifically incorporated herein byreference as background material. Those of ordinary skill in the artwill recognize that other types of crosstalk reduction techniques couldalternatively be implemented.

Identification of Exemplary Photometric and Morphometric Cancer Markers

In the context of the present disclosure, the multi-spectral imagingflow cytometer described above employs UV excitation capabilities andalgorithms to quantitate DNA content and nuclear morphology, for thepurpose of identifying and detecting cancerous and precancerous cells.In addition to employing a flow imaging instrument including a 360 nm UVlaser and an optical system optimized for diffraction-limited imagingperformance in the 400-460 nm (DAPI emission) spectral band, an imagingprocessing system is employed to process the image data. A personalcomputer executing image processing software represents an exemplaryimaging processing system. The imaging processing software incorporatesalgorithms enabling photometric and/or morphometric properties of cellsto be determined based on images of the cells. Exemplary algorithmsinclude masking algorithms, algorithms that define nuclear morphology,algorithms for the quantization of cell cycle histograms, algorithms foranalyzing DNA content, algorithms for analyzing heterochromaticity,algorithms for analyzing N/C ratio, algorithms for analyzinggranularity, algorithms for analyzing CD45 expression, and algorithmsfor analyzing other parameters. In addition, the imaging processingsoftware incorporates an algorithm referred to as a classifier, asoftware based analysis tool that is configured to evaluate a samplepopulation of cells to determine if any disease condition markers arepresent. For determining the presence of cancer cells, the classifierwill analyze the images of the sample population for images havingphotometric and/or morphometric properties corresponding to previouslyidentified photometric and/or morphometric properties associated withcancer cells.

Significantly, for detection of epithelial cell carcinomas, high ratesof data acquisition is required. Such cells have been reported to rangefrom 1 cell in 100,000 peripheral blood leukocytes to 1 cell in1,000,000 peripheral blood leukocytes. The ImageStream™ cytometer andIDEAS™ analytical software package discussed above are ideally suitedfor this application. Imagery from peripheral blood leukocytes can beobtained in the absence of artifacts typical of preparing blood films.Large cell numbers (in the tens and hundreds of thousands) can beaccumulated per sample, providing greater confidence in the analysis ofsubpopulations. Immunofluorescent staining with accepted markers (CD5,CDI9, etc.) can easily be correlated with morphology. The quantitativecell classifiers eliminate the subjectivity of human evaluation, givingcomparisons between patients a degree of confidence previouslyunattainable. Longitudinal studies will also benefit greatly by thequantitative analysis, and the ability to digitally store and retrievelarge numbers of cellular image files, particularly as compared to priorart techniques for the retrieval of microscope slides and/or digitalphotographs of relatively small numbers of cells.

Discrimination of Morphological Features Using Fluorescence-BasedMethodologies

A technology employed in detection of cancer cells in a bodily fluidbased on image data of a population of cells from the bodily fluid wasthe development of preliminary absorbance and fluorescence stainingprotocols for simultaneous morphological analysis of bright field andfluorescence imagery.

Initially, investigations considered the simultaneous use of chromogenicstains and fluorescent dyes. The ability of the imaging system discussedabove to produce bright field imagery, as well as multiple colors offluorescence imagery of each cell, raised the possibility ofsimultaneously employing both traditional chromogenic stains andfluorescent dyes for analysis. However, because chromogenic stains donot normally penetrate cell membranes of viable cells, and because theoptical systems discussed above are able to collect laser side scatterimagery, it was determined that much of the information on cellgranularity that was traditionally acquired via stains, such as Eosin,could be obtained using laser side scatter imagery, without the need forcell staining Numerous cell-permeant fluorescent dyes offer nuclearmorphology without the need for fixing and chromogenic staining. Basedon these considerations, it was determined that fluorescence-basedalternatives for discrimination of morphological features provide abetter approach than traditional staining methodologies.

The primary fluorescence-based alternatives to chromogenic stains usefulin conjunction with the optical systems discussed above are fluorescentDNA binding dyes. A wide variety of such dyes are excitable at 488 nm,including several SYTO dyes (Molecular Probes), DRAQ5 (BioStatus),7-AAD, Propidium Iodide (PI), and others. These dyes are alternatives tochromogenic nuclear stains such as Toluidine Blue, Methyl Green, CrystalViolet, Nuclear Fast Red, Carmalum, Celestine Blue, and Hematoxylin. Afluorescent DNA binding dye is generally included in assay protocolsdeveloped for use with the optical systems described above, for thepurposes of defining the shape and boundaries of the nucleus, its area,its texture (analogous to heterochromaticity), as well as to provide DNAcontent information.

IDEAS™, the software image analysis program discussed above, enablesevaluation of combinations of features from different images of the samecell, in order to expand the utility of the fluorescence nuclear image.For example, the nuclear image mask can be subtracted from the brightfield image mask (which covers the entire cell) as a means forgenerating a mask that includes only the cytoplasmic region. Oncedefined, the cytoplasmic mask can be used to calculate the cytoplasmicarea, the N/C ratio, the relative fluorescence intensity of probes inthe cytoplasm and nucleus, etc., via an intuitive “Feature Manager.” Anexample of a Feature Manager session for the definition of the N/C ratiois shown in FIG. 4. Basic features associated with any cell image areselected from a list and combined algebraically using a simpleexpression builder.

Measurement of Photometric and Morphometric Parameters

In an exemplary implementation of the concepts disclosed herein,ImageStream™ data analysis and cell classification are performedpost-acquisition using the IDEAS™ software package. An annotated IDEAS™software screen capture of an analysis of human peripheral blood isshown in FIG. 5. The IDEAS™ software enables the visualization andphotometric/morphometric analysis of data files containing imagery fromtens of thousands of cells, thereby combining quantitative imageanalysis with the statistical power of flow cytometry.

The exemplary screen shot of FIG. 5 includes images and quantitativedata from 20,000 human peripheral blood mononuclear cells. Whole bloodwas treated with an erythrocyte lysing agent, and the cells were labeledwith an anti-CD45-PerCP mAb (red) and a DNA binding dye (green). Eachcell was imaged in fluorescence using the FL1 and FL4 spectral bands, aswell as dark field and bright field. Images of a plurality of cells in adark field channel 51 a, a green fluorescent channel 51 b, a brightfield channel 51 c, and a red fluorescent channel 51 d can readily beidentified in this Figure. Such a thumbnail image gallery (in the upperleft of the interface) enables the “list mode” inspection of anypopulation of cells. Cell imagery can be pseudo-colored and superimposedfor visualization in the image gallery or enlarged, as shown at thebottom of the interface, for four different cell types (eosinophils 53a, NK cells 53 b, monocytes 53 c, and neutrophils 53 d).

The software also enables one- and two-dimensional plotting of featurescalculated from the imagery. Dots 55 that represent cells in thetwo-dimensional plots can be “clicked” to view the associated imagery inthe gallery. The reverse is true as well. Cell imagery can be selectedto highlight the corresponding dot in every plot in which that cellappears. In addition, gates 57 can be drawn on the plots to definesubpopulations, which can then be inspected in the gallery using a“virtual cell sort” functionality. Any feature calculated from theimagery or defined by the user (i.e., selected from a list of basic andautomatically combined algebraically using a simple expression builder)can be plotted. A dot plot 59 a (displayed at the center left of FIG. 5)shows the clustering resulting from an analysis of CD45 expression(x-axis) versus a dark field granularity metric (y-axis), which issimilar to side-scatter intensity measured in conventional flowcytometry. Plot 59 a reveals lymphocytes (green in a full color image),monocytes (red in a full color image), neutrophils (turquoise in a fullcolor image), and eosinophils (orange in a full color image). A dot plot59 b (displayed at the center right of FIG. 5) substitutes a nucleartexture parameter, “nuclear frequency” for CD45 expression on thex-axis, revealing a putative NK cell population (purple in a full colorimage). Back-displaying the purple population on the left dot plotreveals that this population has the same mean CD45 expression as thelymphocyte population (green on a full color image). The frequencyparameter is one member of the morphologic and photometric feature setthat was developed and incorporated into the IDEAS™ software package.Table 1 below provides an exemplary listing of photometric andmorphometric definitions that can be identified for every image (orsubpopulation, as appropriate). It should be recognized that FIG. 5 hasbeen modified to facilitate its reproduction. As a full-color image, thebackground of each frame including a cell is black, and the backgroundfor each dot plot is black, to facilitate visualization of the cells anddata.

TABLE 1 Morphometric and Photometric Definitions Description ofParameters for Image Features Each Image (6 per object) Area Area ofmask in pixels Aspect Ratio Aspect ratio of mask Aspect RatioIntensity-weighted aspect ratio of mask Intensity Background Mean Meanintensity of pixels outside of mask Intensity Background StdDev Standarddeviation of intensity of pixels outside Intensity of mask Centroid XCentroid of mask in horizontal axis Centmid X IntensityIntensity-weighted centroid of mask in horizontal axis Centroid YCentroid of mask in vertical axis Centmid Y Intensity Intensity-weightedcentroid of mask in vertical axis Combined Mask Total intensity of imageusing logical “OR” of Intensity all six image masks Frequency Varianceof intensity of pixels within mask Gradient Max Maximum intensitygradient of pixels within mask Gradient RMS RMS of intensity gradient ofpixels within mask Intensity Background-corrected sum of pixelintensities within mask Major Axis Major axis of mask in pixels MajorAxis Intensity Intensity-weighted major axis of mask in pixels MeanIntensity Total Intensity of image divided by area of mask MinimumIntensity Minimum pixel intensity within mask Minor Axis Minor axis ofmask in pixels Minor Axis Intensity Intensity-weighted minor axis ofmask in pixels Object Rotation Angle of major axis relative to axis offlow Angle Object Rotation Angle of intensity-weighted major axisrelative Angle Intensity to axis of flow Peak Intensity Maximum pixelintensity within mask Perimeter Number of edge pixels in mask Spot LargeMax Maximum pixel intensity within large bright spots Spot Large TotalSum of pixel intensities within large bright spots Spot Medium MaxMaximum pixel intensity within medium-sized bright spots Spot MediumTotal Sum of pixel intensities within medium-sized bright spots Spot RawMax Un-normalized maximum pixel intensity within large bright spots SpotRaw Total Sum of un-normalized pixel intensities within large brightspots Spot Small Max Maximum pixel intensity within small bright spotsSpot Small Total Sum of pixel intensities within small bright spotsTotal Intensity Sum of pixel intensities within mask Spot Count Numberof spots detected in image Combined Mask Area of logical ‘OR’ of all siximage masks in Area pixels Flow Speed Camera line readout rate in Hertzat time object was imaged Object Number Unique object number SimilarityPixel intensity correlation between two images of the same objectUser-Defined Any algebraic combination of imagery and Features masksUser-Defined Erode, dilate, threshold, Boolean combinations MasksUser-Defined Any Boolean combination of defined Populations populations

Features that quantitate morphology are shown in italics in Table 1.Each feature is automatically calculated for all six types of images(dark field, bright field, and four fluorescent images, that aresimultaneously captured) for each cell, when an image data set is loadedinto the software.

Over 35 features are calculated per image, which amounts to over 200features per cell in assays that employ all six images, not includinguser-defined features. Each cell is also assigned a unique serial numberand time stamp, enabling kinetic studies over cell populations.

Selection of a Photometric/Morphometric Marker for Carcinoma Cells

It was initially proposed that bladder epithelial cells would be used toinvestigate morphometric differences between normal and epithelialcarcinoma cells. However, the initial samples of bladder washings thatwere analyzed revealed that the cell number per sample was highlyvariable, and generally too low to be practical for use in theImageStream™ instrument. Mammary epithelial cells were therefore used inplace of bladder cells. Mammary cells were chosen because normal,primary cells of this kind are commercially available(Clonetics/InVitrogen) and will expand as adherent cells in short-termtissue culture with specialized growth media. In addition, mammaryepithelial carcinoma cells derived from breast cancer metastases areavailable from the American Type Tissue Culture Collection (ATCC). Inorder to better control for tumor to tumor variability, three differentmammary epithelial carcinoma cell lines were studied: HCC-1 500, HCC-1569, and HCC-1428. These lines were established from metastases in threeseparate patients and were purchased from ATCC as frozen stocks. Thecell lines grew adherent to plastic, were expanded by routine tissueculture methods, and used experimentally.

Normal and cancerous mammary epithelial cells were harvested separatelyby brief incubation with trypsin/EDTA at 37 degrees Celsius. The cellswere washed once in cold phosphate buffer solution (PBS) containing 1%FCS, counted, and used experimentally. The three separate mammaryepithelial carcinoma cell lines were pooled in equal proportions for theexperiments described below.

Normal mammary epithelial cells were stained with afluorescein-conjugated monoclonal antibody to the HLA Class I MHC cellsurface protein by incubating the cells with the appropriate,predetermined dilution of the mAb for 30 minutes at 4 degrees C. Despitethe fact that mammary carcinomas are known to down-regulate Class I MHCexpression, as a precaution, the normal cells were fixed in 1%paraformaldehyde to limit passive transfer to the carcinoma cells. Thecombined mammary carcinoma cells lines were also fixed in 1%paraformaldehyde and added to the normal mammary cell population. DRAQ5(BioStatus, Ltd, Leicestershire, UK), a DNA binding dye that can beexcited with a 488 nm laser and emits in the red waveband, was added tothe sample prior to running on the ImageStream™ instrument. The labelingof normal mammary epithelial cells with anti-Class I MHC mAb enabled thenormal cells to be identified in mixes of normal and carcinoma cells,thereby providing an objective “truth” to facilitate the identificationof image features distinguishing normal epithelial cells from epithelialcarcinoma cells.

Mammary carcinomas are known to down-regulate class I MHC expression,but, as a precaution against passive transfer of antibody to thecarcinoma cells, the normal and pooled carcinoma cells were fixedseparately in 1% paraformaldehyde before mixing. DRAQ5, a DNA-bindingdye that can be excited with a 488-nm laser and emits in the redwaveband (BioStatus, Leicestershire, United Kingdom), was added to thesample before running on the ImageStream™, providing DNA content andnuclear morphology features for the analysis.

Image files containing image data of normal mammary epithelial cellsmixed with mammary carcinoma cells were analyzed using the IDEAS™software package with the results described below.

After performing spectral compensation on the data file, an initialvisual inspection was performed to compare normal mammary epithelialcells (positive for class I HLA) to the unstained carcinoma cells.Representative images of normal cells are shown in FIG. 6 andrepresentative carcinoma cells are shown in FIG. 7. Both figures presenteach cell as a row of pseudo-colored images in six channels (left toright): channel 1-blue laser side scatter (dark field); channel 2-blank;channel 3-green HLA-fluorescein-5-isothiocyanate (FITC) fluorescence;channel 4-blank; channel 5-bright field imagery; and channel 6-rednuclear fluorescence. It must be noted that while FIGS. 6 and 7 arebased on full color images, those images have been manipulated tofacilitate reproduction. The colors in the original images have beenreversed (i.e., a negative of the original image was obtained), thenthat negative was converted from a color image to a grayscale image, andcontrast adjustments were performed. Thus, FIGS. 6 and 7 are provided toindicate the types of cellular images that can be acquired, as opposedto faithfully reproducing actual cellular images in their original form.

Thus, representative images of normal cells are shown in FIG. 6, whilerepresentative images of carcinoma cells are shown in FIG. 7. In eachFigure, each horizontal row includes four simultaneously acquired imagesof a single cell. Images in columns 61 a and 71 a correspond to bluelaser side scatter images (i.e., dark field images), images in columns61 b and 71 b correspond to green HLA-FITC fluorescence images, imagesin columns 61 c and 71 c correspond to bright field images, and imagesin columns 61 d and 71 d correspond to red nuclear fluorescence. Asdescribed above, the preferred imaging system is capable ofsimultaneously collecting six different types of images of a single cell(a dark field image, a bright field image, and four fluorescenceimages); in FIGS. 6 and 7, two of the fluorescence channels have notbeen utilized. Once again, it should be recognized that FIGS. 6 and 7have been modified to facilitate their reproduction. As full-colorimages, the backgrounds of FIGS. 6 and 7 are black, images in columns 61a and 71 a are blue, images in columns 61 b and 71 b are green, imagesin columns 61 c and 71 c are grayscale images on a gray background, andimages in columns 61 d and 71 d are red.

When visually comparing full-color images of FIGS. 6 and 7, it isimmediately apparent that images of normal mammary epithelial cells incolumn 61 c (the green fluorescence channel) of FIG. 6 are vivid, whileimages of carcinoma cells in column 71 c (the green fluorescencechannel) of FIG. 7 can hardly be distinguished. It is also apparent thatwhile none of the dark field images (columns 61 a and 71 a) areparticularly intense, the dark field images (column 61 a) of normalmammary epithelial cells in FIG. 6 are significantly more intense thanare the dark field images (column 71 a) of carcinoma cells in FIG. 7.Yet another qualitative observation that can be readily made is that theaverage intensity of the red fluorescence images (column 71 d) ofcarcinoma cells in FIG. 7 is substantially greater than the averageintensity of the red fluorescence images (column 61 d) in FIG. 6.

Initial qualitative observations provided a starting point for theidentification of quantitative features that distinguished the twopopulations. Normal cells were noted to have higher scatter intensityand heterogeneity, generally were larger, and had lower nuclearintensity. The subsequent analysis sought to quantitate thesedifferences, as well as to discover additional parameters that mighthave discrimination capability. A screen capture of the correspondingIDEAS™ analysis is shown in FIG. 8A.

The analysis shown in FIG. 8A proceeded from a dot plot 81 (FIG. 8B) inthe upper left of the Figure. Single cells were first identified, basedon dot plot 81, which was defined as bright field area versus aspectratio. A gate (not separately shown) was drawn around the populationcontaining putative single cells based on the criteria of the area beingsufficiently large to exclude debris, and the aspect ratio being greaterthan −0.5, which eliminates doublets and clusters of cells. The veracityof the gating was tested by examining random cells both within andoutside of the gate using the click-on-a-dot visualizationfunctionality.

Next, the normal mammary cells were distinguished from the mammarycarcinoma cells using the anti-HLA-FITC marker that was applied only tothe normal cells. A solid yellow histogram 85 a (FIG. 8C) of FITCintensity was generated and is shown to the right of dot plot 81. A gate83 was then drawn around the FITC positive (normal mammary epithelialcells) and FITC negative (mammary epithelial carcinoma cells), resultingin a subpopulation of 2031 normal cells, and a subpopulation of 611carcinoma cells. These subpopulations were then used to identifyfeatures that quantitatively discriminated between normal and cancerouscells, based on differential histograms. It should be recognized thatFIG. 8A has been modified to facilitate its reproduction. As afull-color image, the background of each frame including a cell isblack, and the background for each dot plot and histogram is black, tofacilitate visualization of the cells and data. This modificationresulted in the even distribution of dots 81 a, even though such an evendistribution was not present in the full color image.

The remaining ten histograms (i.e., histograms 85 b-85 k; FIGS. 8D-M) inFIG. 8A are differential histograms of the normal cells 87 a (shown asgreen in a full-color image) and carcinoma cells 87 b (shown as red in afull-color image), with each histogram representing a differentquantitative feature. The ten discriminating features fell into fivedistinct classes: scatter intensity, scatter texture, morphology,nuclear intensity, and nuclear texture. Differential histograms 85 b(FIG. 8D), 85 c (FIG. 8E), and 85 d (FIG. 8F) demonstrate the differencebetween the two populations using three different, but correlated,scatter intensity features: “scatter mean intensity” (total intensitydivided by cell area), “scatter intensity” (total intensity minusbackground), and “scatter spot small total” (total intensity of localmaxima). Although all three scatter intensity features provided gooddiscrimination, “scatter mean intensity” (histogram 85 b) was the mostselective.

Differential histograms 85 e (FIG. 8G) and 85 f (FIG. 8H) quantitatedscatter texture using either an intensity profile gradient metric(“scatter gradient RMS”; histogram 85 e; FIG. 8G) or the variance ofpixel intensities (“scatter frequency”; histogram 85 f; FIG. 8H), whichproved more selective.

Differential histograms 85 g (FIG. 8I), 85 h (FIG. 8J) and 85 i (FIG.8K) plotted the cellular area (bright field area, histogram 85 g; FIG.8I), nuclear area (from the DNA fluorescence imagery, histogram 85 h;FIG. 8J), and cytoplasmic area (cellular/nuclear area, histogram 85 i;FIG. 8K). The carcinoma cell lines were generally smaller in brightfield area, confirming the qualitative observations from cell imagery.While the nuclear area of the carcinoma cell lines was proportionatelysmaller than that of the normal cells (e.g. the Nuclear/Cellular arearatio was not discriminatory), the cytoplasmic area was significantlylower in the carcinoma cells.

Finally, differential histograms 85 j (FIG. 8L) and 85 k (FIG. 8M)plotted the nuclear mean intensity (histogram 85 j/FIG. 8L) and nuclearfrequency (heterochromaticity, histogram 85 k; FIG. 8M), respectively.As in the case of scatter, both of these features provided somediscriminatory power.

The multispectral/multimodal imagery collected by the ImageStream™instrument and analyzed using the IDEAS™ software package in thisengineered experiment revealed a number of significant differences indark field scatter, morphology, and nuclear staining between normalepithelial and epithelial carcinoma cells. While it is well-recognizedthat cells adapted to tissue culture have undergone a selection processthat may have altered their cellular characteristics, these datademonstrate that it is feasible to build an automated classifier thatuses the morphometric and photometric features identified and describedabove to separate normal from transformed epithelial cells, and possiblyother cell types.

A further experimental investigation analyzed image data collected froma mixture of normal peripheral blood cells and mammary carcinoma cells.As shown in FIG. 5 (discussed above), cell classification of humanperipheral blood can be achieved using a flow imaging system configuredto simultaneously obtain a plurality of images of each cell, and usingan automatic image analysis program (with the ImageStream™ instrumentrepresenting an exemplary imaging system, and the IDEAS™ softwarepackage representing an exemplary image analysis program). Using CD45expression combined with an analysis of dark field light scatterproperties, cells can be separated into five distinct populations basedon the image data collected by the flow imaging system: lymphocytes,monocytes, neutrophils, eosinophils and basophils. This separation ofhuman peripheral blood into distinct subpopulations is shown in greaterdetail in FIG. 9, which includes exemplary relative abundance data forthe different subpopulations. The veracity of the classifications wasdetermined by using population-specific monoclonal antibody markers andbackgating marker-positive cells on the scatter vs. CD45 plot, as wellas morphological analysis of the associated imagery. The x-axis of thegraph in FIG. 9 corresponds to anti-CD45-FITC Intensity, while they-axis corresponds to dark field scatter intensity.

In order to determine whether the techniques disclosed herein (utilizingthe flow imaging instrument system described above, which is exemplifiedby the ImageStream™ instrument, and imaging analysis software, which isexemplified by the IDEAS™ software package) could discriminateepithelial carcinoma cells from normal PBMC, an artificial mixture oftumor cells and normal PBMC was produced as described above. The cellmixture was labeled with an anti-CD45-FITC mAb and a fluorescent DNAbinding dye in order to differentiate PBMC subpopulations, generally asdescribed above. A comparison of the scatter vs. CD45 bivariate plotsfor normal peripheral blood mononuclear cells and the PBMC sample spikedwith the carcinoma cells is shown in FIGS. 10A and 10B. FIG. 10Agraphically illustrates a distribution of normal peripheral bloodmononuclear cells (PBMC) based on image data collected from a populationof cells that does not include mammary carcinoma cells. FIG. 10Bgraphically illustrates a distribution of normal PBMC and mammarycarcinoma cells based on image data collected from a population of cellsthat includes both cell types, illustrating how the distribution of themammary carcinoma cells is distinguishable from the distribution of thenormal PBMC. In this analysis, carcinoma cells 101 a fall well outsideof a normally defined PBMC population 101 b, as confirmed by visualinspection of the outlier population.

As shown in FIGS. 11A and 11B, carcinoma cells 111 a can also bediscriminated from normal PBMC 111 b using some of the morphometric andphotometric features identified in FIGS. 8A-8M (e.g., nuclear area,cytoplasmic area, scatter intensity, and scatter frequency). FIG. 11Agraphically illustrates a distribution of normal PBMC and mammarycarcinoma cells based on measured cytoplasmic area derived from imagedata collected from a population of cells that includes both cell types,illustrating how the distribution of cytoplasmic area of mammarycarcinoma cells is distinguishable from the distribution of cytoplasmicarea of the normal PBMC. FIG. 11B graphically illustrates a distributionof normal PBMC and mammary carcinoma cells based on measured scatterfrequency derived from image data collected from a population of cellsthat includes both cell types, illustrating how the distribution of thescatter frequency of the mammary carcinoma cells is distinguishable fromthe distribution of the scatter frequency of the normal PBMC. Althoughthese features were initially identified for the purpose ofdiscriminating between normal mammary and mammary carcinoma cells, theyprovide a high level of discrimination between mammary epithelialcarcinoma cells and PBMC. Significantly, normal epithelial cells wouldbe even more clearly differentiated from PBMC and distinct from theepithelial carcinoma cells using these parameters.

It should be recognized that FIGS. 10A, 10B, 11A, and 11B have beenmodified to facilitate their reproduction. As a full-color images, thebackground of each frame including a dot plot is black, to facilitatevisualization of the cells and/or data, and dots representing PBMC andcarcinoma cells are different colors.

The results noted above were verified by visual inspection of thesegregated images (i.e., the images separated into subpopulationscorresponding to carcinoma cells and healthy cells using one or more ofthe above-identified photometric and/or morphometric parameters). Imagegallery data were produced from the spiked PBMC data described above.FIG. 12 includes representative images from the carcinoma cellpopulation, obtained using an overlay composite of bright field andDRAQ5 DNA fluorescence (red, with the image processing being performedby the image analysis software). FIG. 13 includes images of the fiveperipheral blood mononuclear cell populations defined using dark fieldscatter, CD45 (green), and DRAQ5 (red) for nuclear morphology. Note thatthe two Figures are at different size scales. It should be recognizedthat FIG. 12 has been modified to facilitate its reproduction. As afull-color image, the background of FIG. 12 is black, the background ofeach frame including a cell is brown/gray, and the nucleus of each cellis red. FIG. 13 has been similarly modified to facilitate itsreproduction. As a full-color image, the background of FIG. 13 is black,the periphery of each cell is green, and the nucleus of each cell isred.

Significantly, the above studies demonstrate the feasibility ofoptically discriminating a subpopulation of normal epithelial cells froma subpopulation of transformed cells by analyzingmulti-spectral/multimodal image data from a mixed population of suchcells, where the image data are simultaneously collected. The abovestudies also demonstrate the feasibility of detecting epithelialcarcinoma cells in blood by analyzing multi-spectral/multimodal imagedata from a mixed population of such cells, where the image data aresimultaneously collected.

Exemplary Computing Environment

As noted above, an aspect of the present invention involves imageanalysis of a plurality of images simultaneously collected from membersof the population of cells. Reference has been made to an exemplaryimage analysis software package. FIG. 14 and the following relateddiscussion are intended to provide a brief, general description of asuitable computing environment for practicing the present invention,where the image processing required is implemented using a computingdevice generally like that shown in FIG. 14. Those skilled in the artwill appreciate that the required image processing may be implemented bymany different types of computing devices, including a laptop and othertypes of portable computers, multiprocessor systems, networkedcomputers, mainframe computers, hand-held computers, personal dataassistants (PDAs), and on other types of computing devices that includea processor and a memory for storing machine instructions, which whenimplemented by the processor, result in the execution of a plurality offunctions.

An exemplary computing system 150 suitable for implementing the imageprocessing required in the present invention includes a processing unit154 that is functionally coupled to an input device 152, and an outputdevice 162, e.g., a display. Processing unit 154 include a centralprocessing unit (CPU 158) that executes machine instructions comprisingan image processing/image analysis program for implementing thefunctions of the present invention (analyzing a plurality of imagessimultaneously collected for members of a population of objects toenable at least one characteristic exhibited by members of thepopulation to be measured). In at least one embodiment, the machineinstructions implement functions generally consistent with thosedescribed above, with reference to the flowchart of FIG. 3, as well asthe exemplary screenshots. Those of ordinary skill in the art willrecognize that processors or central processing units (CPUs) suitablefor this purpose are available from Intel Corporation, AMD Corporation,Motorola Corporation, and from other sources.

Also included in processing unit 154 are a random access memory 156(RAM) and non-volatile memory 160, which typically includes read onlymemory (ROM) and some form of memory storage, such as a hard drive,optical drive, etc. These memory devices are bi-directionally coupled toCPU 158. Such storage devices are well known in the art. Machineinstructions and data are temporarily loaded into RAM 156 fromnon-volatile memory 160. Also stored in memory are the operating systemsoftware and ancillary software. While not separately shown, it shouldbe understood that a power supply is required to provide the electricalpower needed to energize computing system 150.

Input device 152 can be any device or mechanism that facilitates inputinto the operating environment, including, but not limited to, a mouse,a keyboard, a microphone, a modem, a pointing device, or other inputdevices. While not specifically shown in FIG. 14, it should beunderstood that computing system 150 is logically coupled to an imagingsystem such as that schematically illustrated in FIG. 1A, so that theimage data collected are available to computing system 150 to achievethe desired image processing. Of course, rather than logically couplingthe computing system directly to the imaging system, data collected bythe imaging system can simply be transferred to the computing system bymeans of many different data transfer devices, such as portable memorymedia, or via a network (wired or wireless). Output device 162 will mosttypically comprise a monitor or computer display designed for humanvisual perception of an output image.

High-Throughput, EDF Imaging of Cells Subjected to in situ Hybridization

Imaging flow cytometry is compatible with the broad range ofcell-staining protocols developed for conventional flow cytometry andthose developed for imaging cells on slides, although with protocolmodifications to the suspension format. Fluorescence in situhybridization (FISH) is recognized as a slide-based imaging applicationthat could benefit greatly from the greater throughput and quantitativeidentification of flow cytometry; several groups have adaptedhybridization techniques to cells in suspension. The lack of spatialresolution in standard flow cytometry, however, requires thesubstitution of total probe intensity for spot counting as a means ofassessing results, thereby preventing the use of flow cytometry for theanalysis of translocations, inversions, or other rearrangements.Although there are certain specific FISH applications that have strongand consistent signals, such as telomeric length analysis or thedetection of the presence or absence of a Y chromosome, FISH probeintensity variation can be high and signal intensities often approachthe detection limits of standard flow cytometry, reducing thereliability of aneuploidy assessment.

Imaging flow cytometry is potentially well suited to FISH analysisbecause the detection limit of imaging flow cytometry improves as thesize of the fluorescent signal source decreases. Further, thequantitative capabilities of FISH-probed cells for applications such asaneuploidy analysis, is accomplished by spot counting rather than byrelying exclusively on total intensity analysis, making it tolerant ofwide variations in probe intensity and more consistent with the standardof practice in clinical FISH assessment.

To investigate the usefulness of imaging flow cytometry for clinicalFISH analysis, human peripheral blood mononuclear cells (PBMC) wereobtained (AllCells, Emeryville, Calif.) and probed using a FISH insuspension (FISHIS) protocol developed at Amnis Corporation. The cellswere fixed and permeabilized with successive incubations in 30% Carnoy'ssolution in PBS (30 minutes at 4° C.), then 70% Carnoy's solution in PBS(10 minutes at 4° C.). After centrifugation, the cells were washed oncein 2× saline sodium citrate (SSC), then resuspended in hybridizationbuffer containing the SpectrumGreen-labeled chromosome 12 enumerationprobe according to the manufacturer's directions (Vysis, Des Plaines,Ill.). To hybridize the probe, cells in polymerase chain reaction tubeswere exposed to 80° C. for 5 minutes and 42° C. for 2 hours in a DNAthermocycler. One hundred micro-liters of 2×SSC was added to the tubesand the cells pelleted by centrifugation. Cells were resuspended in0.4×SSC containing 0.3% NP40 and exposed to 72° C. for 2 minutes. Thecells were centrifuged and the pellets then resuspended in 50micro-liters of 1% paraformaldehyde (in PBS). The sample was then loadedinto the ImageStream™ system, and a file of 3500 cells was collected.

FIG. 15 is a gallery of 15 individual cells from the PBMC data file,numbered by the order in which they flowed through the instrument. Eachcell is represented by a row of images (left to right): dark field,chromosome 12 fluorescence, bright field, and an overlay of thefluorescence and bright-field images. Doublets and larger clusters wereeliminated from the analysis by plotting the area versus the aspectratio of each cell's bright field image on a dot plot and gating onsingle cells, which represented approximately 60% of the data and weredifferentiated clearly as a population having an aspect(length-to-width) ratio close to one and lower area than doublets andclusters. No other pre-selection was performed, so the galleryrepresents an unbiased sampling of FISH data in PBMC populations. Mostcells had two well-resolved FISH spots, corresponding to the two copiesof chromosome 12. A fraction of the cells, however, had one or both FISHspots out of focus to some degree or only one apparent spotcorresponding to a cell orientation that superimposed the FISH spotsfrom the perspective of the imaging system. Defocus is a problem thatincreases with cell size, whereas the frequency of FISH spotsuperposition tends to decrease as cell size increases. The cells in thePBMC data file were found to have a mean diameter of 6.4±0.7micrometers, which is small compared with the nuclear size of manyepithelial cell types.

It should be recognized that FIG. 15 has been modified to facilitate itsreproduction. As a full-color image, the background of FIG. 15 is black,the background of each frame including a cell is brown/gray, thecellular images in the dark field column are blue, and the FISH spots inthe second and fourth columns are green (not purple).

To address the constraint that limited depth of field places on FISHanalysis in larger cells assessed by imaging flow cytometry, a prototypeImageStream™ system having extended depth-of-field (EDF) imagecollection capabilities was developed. The EDF version of theImageStream™ system incorporates a specialized optical element in thestandard optical system that causes light from widely different focalpositions in the object to be imaged on the detector planesimultaneously in a process referred to as Wavefront Coding™ by itsdeveloper (CDM Optics, Boulder, Colo.). The modified imagery is postprocessed to recover image sharpness while preserving the increaseddepth of focus that comes from the modification of the wavefront duringdata acquisition. Images acquired using the EDF version of the systemhave an effective depth of field of approximately 15 micrometers,resulting in a high-resolution image of the cell with all featuressimultaneously in focus. Unlike confocal image stacking techniques, theWavefront Coding™ methodology enables image acquisition at rates ofhundreds of cells per second.

To compare FISH imagery in the standard and EDF ImageStream™con-figurations, Jurkat human lymphoma cells (ATCC) were grown insuspension culture, hybridized to a chromosome 8 probe (Vysis) using theFISHIS protocol (described previously), and imaged using the EDF andstandard ImageStream™ configurations. FIGS. 16 and 17 illustrategalleries of standard and EDF images, respectively, of hybridized Jurkatcells classified as disomic for chromosome 8. In both galleries, eachcell is represented by an overlay of its FISH spot fluorescence image(green) on its reduced-contrast bright field image acquired at the sametime. Because Jurkats are known to exhibit cytogenetic instability, onlysingle cells were included in each gallery based on their automatedclassification as having two chromosome 8 FISH spots, but no subjectiveselection criteria were used in selecting the 25 images shown in eachFigure. The degree of FISH spot focus enhancement with EDF imaging isqualitatively evident and improves the fidelity of automated spotanalysis features (peak intensity, spot size, mean separation distance,and so forth) by as much as tenfold.

Although the concepts disclosed herein have been described in connectionwith the preferred form of practicing them and modifications thereto,those of ordinary skill in the art will understand that many othermodifications can be made thereto within the scope of the claims thatfollow. Accordingly, it is not intended that the scope of these conceptsin any way be limited by the above description, but instead bedetermined entirely by reference to the claims that follow.

The invention in which an exclusive right is claimed is defined by the following:
 1. A method for detecting an abnormal condition of cells included in a sample of cells, comprising: (a) comparing image data for a plurality of simultaneously collected images of individual known normal cells with image data for a plurality of simultaneously collected images of individual known abnormal cells to identify at least one characteristic that can be measured and which distinguishes a known abnormal cell from a known normal cell; (b) imaging the sample of cells to collect sample imaging data for a plurality of simultaneously collected images of individual cells in the sample cells, wherein the simultaneously collected images for the known normal cells, the known abnormal cells, and the sample cells each include at least one of the following two types of images: (i) multispectral images; and (ii) multimodal images; and (c) analyzing the sample image data to determine if any cells in the sample of cells exhibit the abnormal condition, by detecting the at least one cell characteristic that distinguishes the known abnormal cells from the known normal cells.
 2. The method of claim 1, wherein identifying the at least one cell characteristic comprises identifying a photometric parameter.
 3. The method of claim 1, wherein identifying the at least one cell characteristic comprises identifying a morphometric parameter.
 4. The method of claim 3, wherein identifying the morphometric parameter comprises identifying a cytoplasmic area.
 5. The method of claim 4, wherein identifying a cytoplasmic area comprises: (a) using a bright field image to determine a cellular area; (b) using a fluorescent image to determine a nuclear area; and (c) defining the cytoplasmic area as a difference between the cellular area and the nuclear area.
 6. The method of claim 1, wherein identifying the at least one cell characteristic comprises identifying at least one of the following characteristics: (a) a scatter intensity; (b) a scatter texture; (c) a nuclear intensity; and (d) a nuclear texture.
 7. The method of claim 6, wherein if the at least one cell characteristic that is identified includes the scatter intensity, identifying the scatter intensity comprises determining a mean scatter intensity by dividing a total intensity by a cellular area.
 8. The method of claim 6, wherein if the at least one cell characteristic that is identified includes the scatter intensity, identifying the scatter intensity comprises calculating the scatter intensity by removing a background intensity from a total intensity.
 9. The method of claim 6, wherein if the at least one cell characteristic that is identified includes the scatter intensity, identifying the scatter intensity comprises calculating the scatter intensity by determining a total intensity of local scatter maxima.
 10. The method of claim 6, wherein if the at least one cell characteristic that is identified includes the scatter texture, identifying the scatter texture comprises at least one selected from the group of: (a) identifying an intensity profile gradient metric; and (b) identifying a variance of pixel intensities.
 11. The method of claim 1, wherein each of the simultaneously collected images for the known normal cells, the known abnormal cells, and the sample cells comprise extended depth of field images.
 12. The method of claim 1, wherein each of the simultaneously collected images for the known normal cells, the known abnormal cells, and the sample cells comprise at least two types of images selected from the group consisting of: (a) a bright field image; (b) a dark field image; and (c) a fluorescent image.
 13. An imaging system configured to acquire and analyze image data collected from a sample of cells, where the image data include a plurality of images of individual cells that are acquired simultaneously, to enable detection of any cells within the sample in which an abnormal condition exists, comprising: (a) an image acquisition subsystem that simultaneously produces a plurality of images of individual cells in the sample of cells, the plurality images including at least one of the following types of images: (i) multispectral images; and (ii) multimodal images; (b) data identifying at least one cell characteristic indicative of the abnormal condition, where the at least one cell characteristic can be measured using the plurality of images produced by the image acquisition subsystem; and (c) a computing device used to analyze the plurality of images of individual cells to determine if the at least one cell characteristic is exhibited by any cells in the sample of cells.
 14. The imaging system of claim 13, wherein the at least one characteristic comprises a photometric parameter.
 15. The imaging system of claim 13, wherein the at least one characteristic comprises a morphometric parameter.
 16. The imaging system of claim 15, wherein the morphometric parameter comprises cytoplasmic area.
 17. The imaging system of claim 13, wherein the at least one characteristic comprises at least one characteristic selected from the group consisting of: (a) a scatter intensity; (b) a scatter texture; (c) a nuclear intensity; and (d) a nuclear texture.
 18. The imaging system of claim 17, wherein if the at least one characteristic includes scatter intensity, the scatter intensity includes at least one element selected from the group consisting of: (a) a mean scatter intensity, the mean scatter intensity being calculated using a total intensity divided by a cellular area; (b) a scatter intensity calculated by removing a background intensity from a total intensity; and (c) a scatter intensity calculated by determining a total intensity of local scatter maxima.
 19. An imaging system for determining whether any cells in a sample of cells exhibit an abnormal condition, comprising: (a) a plurality of lenses for collecting and focusing light from an individual cell in a plurality of cells included within the sample; (b) a spectral separation element that spectrally separates the light from the individual cell into a plurality of different spectral channels; (c) a light detector that receives the light in the different spectral channels, at least one of the plurality of lenses imaging the light in each different spectral channel on a different portion of the detector, so that the detector simultaneously produces image data for a plurality of images comprising the light in the plurality of different spectral channels; and (d) a computing device that analyzes the images data to determine if the image data indicates that the individual cell exhibits the abnormal condition.
 20. The imaging system of claim 19, wherein the plurality of images include at least two types of images selected from the group consisting of: (a) a bright field image; (b) a dark field image; and (c) a fluorescent image. 